Patents by Inventor Hesam Fathi Moghadam
Hesam Fathi Moghadam has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
-
Patent number: 11966275Abstract: The present invention relates to machine learning (ML) explainability (MLX). Herein are local explanation techniques for black box ML models based on coalitions of features in a dataset. In an embodiment, a computer receives a request to generate a local explanation of which coalitions of features caused an anomaly detector to detect an anomaly. During unsupervised generation of a new coalition, a first feature is randomly selected from features in a dataset. Which additional features in the dataset can join the coalition, because they have mutual information with the first feature that exceeds a threshold, is detected. For each feature that is not in the coalition, values of the feature are permuted in imperfect copies of original tuples in the dataset. An average anomaly score of the imperfect copies is measured. Based on the average anomaly score of the imperfect copies, a local explanation is generated that references (e.g. defines) the coalition.Type: GrantFiled: November 22, 2022Date of Patent: April 23, 2024Assignee: Oracle International CorporationInventors: Ali Seyfi, Yasha Pushak, Hesam Fathi Moghadam, Sungpack Hong, Hassan Chafi
-
Publication number: 20240095580Abstract: Herein is a universal anomaly threshold based on several labeled datasets and transformation of anomaly scores from one or more anomaly detectors. In an embodiment, a computer meta-learns from each anomaly detection algorithm and each labeled dataset as follows. A respective anomaly detector based on the anomaly detection algorithm is trained based on the dataset. The anomaly detector infers respective anomaly scores for tuples in the dataset. The following are ensured in the anomaly scores from the anomaly detector: i) regularity that an anomaly score of zero cannot indicate an anomaly and ii) normality that an inclusive range of zero to one contains the anomaly scores from the anomaly detector. A respective anomaly threshold is calculated for the anomaly scores from the anomaly detector. After all meta-learning, a universal anomaly threshold is calculated as an average of the anomaly thresholds. An anomaly is detected based on the universal anomaly threshold.Type: ApplicationFiled: November 28, 2022Publication date: March 21, 2024Inventors: Yasha Pushak, Hesam Fathi Moghadam, Anatoly Yakovlev, Robert David Hopkins, II
-
Publication number: 20240095604Abstract: A computer sorts empirical validation scores of validated training scenarios of an anomaly detector. Each training scenario has a dataset to train an instance of the anomaly detector that is configured with values for hyperparameters. Each dataset has values for metafeatures. For each predefined ranking percentage, a subset of best training scenarios is selected that consists of the ranking percentage of validated training scenarios having the highest empirical validation scores. Linear optimizers train to infer a value for a hyperparameter. Into many distinct unvalidated training scenarios, a scenario is generated that has metafeatures values and hyperparameters values that contains the value inferred for that hyperparameter by a linear optimizer. For each unvalidated training scenario, a validation score is inferred. A best linear optimizer is selected having a highest combined inferred validation score. For a new dataset, the best linear optimizer infers a value of that hyperparameter.Type: ApplicationFiled: December 6, 2022Publication date: March 21, 2024Inventors: Fatjon Zogaj, Yasha Pushak, Hesam Fathi Moghadam, Sungpack Hong, Hassan Chafi
-
Publication number: 20240095231Abstract: In a computer, each of multiple anomaly detectors infers an anomaly score for each of many tuples. For each tuple, a synthetic label is generated that indicates for each anomaly detector: the anomaly detector, the anomaly score inferred by the anomaly detector for the tuple and, for each of multiple contamination factors, the contamination factor and, based on the contamination factor, a binary class of the anomaly score. For each particular anomaly detector excluding a best anomaly detector, a similarity score is measured for each contamination factor. The similarity score indicates how similar, between the particular anomaly detector and the best anomaly detector, are the binary classes of labels with that contamination factor. For each contamination factor, a combined similarity score is calculated based on the similarity scores for the contamination factor.Type: ApplicationFiled: December 6, 2022Publication date: March 21, 2024Inventors: Yasha Pushak, Constantin Le Clei, Fatjon Zogaj, Hesam Fathi Moghadam, Sungpack Hong, Hassan Chafi
-
Publication number: 20240070471Abstract: Principal component analysis (PCA) accelerates and increases accuracy of genetic algorithms. In an embodiment, a computer generates many original chromosomes. Each original chromosome contains a sequence of original values. Each position in the sequences in the original chromosomes corresponds to only one respective distinct parameter in a set of parameters to be optimized. Based on the original chromosomes, many virtual chromosomes are generated. Each virtual chromosome contains a sequence of numeric values. Positions in the sequences in the virtual chromosomes do not correspond to only one respective distinct parameter in the set of parameters to be optimized. Based on the virtual chromosomes, many new chromosomes are generated. Each new chromosome contains a sequence of values. Each position in the sequences in the new chromosomes corresponds to only one respective distinct parameter in the set of parameters to be optimized. The computer may be configured based on a best new chromosome.Type: ApplicationFiled: August 31, 2022Publication date: February 29, 2024Inventors: Yasha Pushak, Moein Owhadi Kareshk, Hesam Fathi Moghadam, Sungpack Hong, Hassan Chafi
-
Publication number: 20230376366Abstract: The present invention relates to machine learning (ML) explainability (MLX). Herein are local explanation techniques for black box ML models based on coalitions of features in a dataset. In an embodiment, a computer receives a request to generate a local explanation of which coalitions of features caused an anomaly detector to detect an anomaly. During unsupervised generation of a new coalition, a first feature is randomly selected from features in a dataset. Which additional features in the dataset can join the coalition, because they have mutual information with the first feature that exceeds a threshold, is detected. For each feature that is not in the coalition, values of the feature are permuted in imperfect copies of original tuples in the dataset. An average anomaly score of the imperfect copies is measured. Based on the average anomaly score of the imperfect copies, a local explanation is generated that references (e.g. defines) the coalition.Type: ApplicationFiled: November 22, 2022Publication date: November 23, 2023Inventors: Ali Seyfi, Yasha Pushak, Sungpack Hong, Hesam Fathi Moghadam, Hassan Chafi
-
Publication number: 20230334364Abstract: In an embodiment in a computer, each of several anomaly detectors infers a respective anomaly inference for each of many test tuples. For each available anomaly detector that is not the candidate anomaly detector, a respective fitness score is measured for the candidate anomaly detector that indicates how similar are anomaly inferences of the candidate anomaly detector to anomaly inferences of the available anomaly detector. Fitness scores of the candidate anomaly detector are combined into a combined fitness score for the candidate anomaly detector. The best anomaly detector that has a highest combined fitness score is selected for further operation such as inferring an anomaly inference for a new tuple while retraining or in production.Type: ApplicationFiled: December 6, 2022Publication date: October 19, 2023Inventors: Yasha Pushak, Robert Wayne Harlow, Constantin Le Clei, Hesam Fathi Moghadam, Sungpack Hong, Hassan Chafi
-
Publication number: 20230153394Abstract: Herein are timeseries preprocessing, model selection, and hyperparameter tuning techniques for forecasting development based on temporal statistics of a timeseries and a single feed-forward pass through a machine learning (ML) pipeline. In an embodiment, a computer hosts and operates the ML pipeline that automatically measures temporal statistic(s) of a timeseries. ML algorithm selection, cross validation, and hyperparameters tuning is based on the temporal statistics of the timeseries. The result from the ML pipeline is a rigorously trained and production ready ML model that is validated to have increased accuracy for multiple prediction horizons. Based on the temporal statistics, efficiency is achieved by asymmetry of investment of computer resources in the tuning and training of the most promising ML algorithm(s). Compared to other approaches, this ML pipeline produces a more accurate ML model for a given amount of computer resources and consumes fewer computer resources to achieve a given accuracy.Type: ApplicationFiled: November 17, 2021Publication date: May 18, 2023Inventors: Ritesh Ahuja, Anatoly Yakovlev, Venkatanathan Varadarajan, Sandeep R. Agrawal, Hesam Fathi Moghadam, Sanjay Jinturkar, Nipun Agarwal
-
Publication number: 20230139718Abstract: Herein are acceleration and increased reliability based on classification and scoring techniques for machine learning that compare two similar datasets of different ages to detect data drift without a predefined drift threshold. Various subsets are randomly sampled from the datasets. The subsets are combined in various ways to generate subsets of various age mixtures. In an embodiment, ages are permuted and drift is detected based on whether or not fitness scores indicate that an age binary classifier is confused. In an embodiment, an anomaly detector measures outlier scores of two subsets of different age mixtures. Drift is detected when the outlier scores diverge. In a two-arm bandit embodiment, iterations randomly alternate between both datasets based on respective probabilities that are adjusted by a bandit reward based on outlier scores from an anomaly detector. Drift is detected based on the probability of the younger dataset.Type: ApplicationFiled: October 28, 2021Publication date: May 4, 2023Inventors: Mojtaba Valipour, Yasha Pushak, Robert Harlow, Hesam Fathi Moghadam, Sungpack Hong, Hassan Chafi
-
Patent number: 11620568Abstract: Techniques are provided for selection of machine learning algorithms based on performance predictions by using hyperparameter predictors. In an embodiment, for each mini-machine learning model (MML model), a respective hyperparameter predictor set that predicts a respective set of hyperparameter settings for a data set is trained. Each MML model represents a respective reference machine learning model (RML model). Data set samples are generated from the data set. Meta-feature sets are generated, each meta-feature set describing a respective data set sample. A respective target set of hyperparameter settings are generated for said each MML model using a hypertuning algorithm. The meta-feature sets and the respective target set of hyperparameter settings are used to train the respective hyperparameter predictor set. Each hyperparameter predictor set is used during training and inference to improve the accuracy of automatically selecting a RML model per data set.Type: GrantFiled: April 18, 2019Date of Patent: April 4, 2023Assignee: Oracle International CorporationInventors: Hesam Fathi Moghadam, Sandeep Agrawal, Venkatanathan Varadarajan, Anatoly Yakovlev, Sam Idicula, Nipun Agarwal
-
Patent number: 11615265Abstract: The present invention relates to dimensionality reduction for machine learning (ML) models. Herein are techniques that individually rank features and combine features based on their rank to achieve an optimal combination of features that may accelerate training and/or inferencing, prevent overfitting, and/or provide insights into somewhat mysterious datasets. In an embodiment, a computer ranks features of datasets of a training corpus. For each dataset and for each landmark percentage, a target ML model is configured to receive only a highest ranking landmark percentage of features, and a landmark accuracy achieved by training the ML model with the dataset is measured. Based on the landmark accuracies and meta-features values of the dataset, a respective training tuple is generated for each dataset. Based on all of the training tuples, a regressor is trained to predict an optimal amount of features for training the target ML model.Type: GrantFiled: August 21, 2019Date of Patent: March 28, 2023Assignee: Oracle International CorporationInventors: Tomas Karnagel, Sam Idicula, Hesam Fathi Moghadam, Nipun Agarwal
-
Patent number: 11429895Abstract: Herein are techniques for exploring hyperparameters of a machine learning model (MLM) and to train a regressor to predict a time needed to train the MLM based on a hyperparameter configuration and a dataset. In an embodiment that is deployed in production inferencing mode, for each landmark configuration, each containing values for hyperparameters of a MLM, a computer configures the MLM based on the landmark configuration and measures time spent training the MLM on a dataset. An already trained regressor predicts time needed to train the MLM based on a proposed configuration of the MLM, dataset meta-feature values, and training durations and hyperparameter values of landmark configurations of the MLM. When instead in training mode, a regressor in training ingests a training corpus of MLM performance history to learn, by reinforcement, to predict a training time for the MLM for new datasets and/or new hyperparameter configurations.Type: GrantFiled: April 15, 2019Date of Patent: August 30, 2022Assignee: Oracle International CorporationInventors: Anatoly Yakovlev, Venkatanathan Varadarajan, Sandeep Agrawal, Hesam Fathi Moghadam, Sam Idicula, Nipun Agarwal
-
Publication number: 20220138504Abstract: In an embodiment based on computer(s), an ML model is trained to detect outliers. The ML model calculates anomaly scores that include a respective anomaly score for each item in a validation dataset. The anomaly scores are automatically organized by sorting and/or clustering. Based on the organized anomaly scores, a separation is measured that indicates fitness of the ML model. In an embodiment, a computer performs two-clustering of anomaly scores into a first organization that consists of a first normal cluster of anomaly scores and a first anomaly cluster of anomaly scores. The computer performs three-clustering of the same anomaly scores into a second organization that consists of a second normal cluster of anomaly scores, a second anomaly cluster of anomaly scores, and a middle cluster of anomaly scores. A distribution difference between the first organization and the second organization is measured. An ML model is processed based on the distribution difference.Type: ApplicationFiled: October 29, 2020Publication date: May 5, 2022Inventors: Hesam Fathi Moghadam, Anatoly Yakovlev, Sandeep Agrawal, Venkatanathan Varadarajan, Robert Hopkins, Matteo Casserini, Milos Vasic, Sanjay Jinturkar, Nipun Agarwal
-
Publication number: 20220121955Abstract: Herein, a computer generates and evaluates many preprocessor configurations for a window preprocessor that transforms a training timeseries dataset for an ML model. With each preprocessor configuration, the window preprocessor is configured. The window preprocessor then converts the training timeseries dataset into a configuration-specific point-based dataset that is based on the preprocessor configuration. The ML model is trained based on the configuration-specific point-based dataset to calculate a score for the preprocessor configuration. Based on the scores of the many preprocessor configurations, an optimal preprocessor configuration is selected for finally configuring the window preprocessor, after which, the window preprocessor can optimally transform a new timeseries dataset such as in an offline or online production environment such as for real-time processing of a live streaming timeseries.Type: ApplicationFiled: October 15, 2020Publication date: April 21, 2022Inventors: Nikan Chavoshi, Anatoly Yakovlev, Hesam Fathi Moghadam, Venkatanathan Varadarajan, Sandeep Agrawal, Ali Moharrer, Jingxiao Cai, Sanjay Jinturkar, Nipun Agarwal
-
Publication number: 20210390466Abstract: A proxy-based automatic non-iterative machine learning (PANI-ML) pipeline is described, which predicts machine learning model configuration performance and outputs an automatically-configured machine learning model for a target training dataset. Techniques described herein use one or more proxy models—which implement a variety of machine learning algorithms and are pre-configured with tuned hyperparameters—to estimate relative performance of machine learning model configuration parameters at various stages of the PANI-ML pipeline. The PANI-ML pipeline implements a radically new approach of rapidly narrowing the search space for machine learning model configuration parameters by performing algorithm selection followed by algorithm-specific adaptive data reduction (i.e., row- and/or feature-wise dataset sampling), and then hyperparameter tuning.Type: ApplicationFiled: October 30, 2020Publication date: December 16, 2021Inventors: Venkatanathan Varadarajan, Sandeep R. Agrawal, Hesam Fathi Moghadam, Anatoly Yakovlev, Ali Moharrer, Jingxiao Cai, Sanjay Jinturkar, Nipun Agarwal, Sam Idicula, Nikan Chavoshi
-
Publication number: 20210257317Abstract: Distributions of on-chip inductors for monolithic voltage regulation are described. On-chip voltage regulation may be provided by integrated voltage regulators (IVRs), such as a buck converter with integrated inductors. On-chip inductors may be placed to ensure optimal voltage regulation for high power density applications. With this technology, integrated circuits may have many independent voltage domains for fine-grained dynamic voltage and frequency scaling that allows for higher overall power efficiency for the system.Type: ApplicationFiled: May 3, 2021Publication date: August 19, 2021Inventors: Michael Henry Soltau Dayringer, Anatoly Yakovlev, Ji Eun Jang, Hesam Fathi Moghadam, David Hopkins
-
Patent number: 11024589Abstract: Distributions of on-chip inductors for monolithic voltage regulation are described. On-chip voltage regulation may be provided by integrated voltage regulators (IVRs), such as a buck converter with integrated inductors. On-chip inductors may be placed to ensure optimal voltage regulation for high power density applications. With this technology, integrated circuits may have many independent voltage domains for fine-grained dynamic voltage and frequency scaling that allows for higher overall power efficiency for the system.Type: GrantFiled: October 12, 2018Date of Patent: June 1, 2021Assignee: Oracle International CorporationInventors: Michael Henry Soltau Dayringer, Anatoly Yakovlev, Ji Eun Jang, Hesam Fathi Moghadam, David Hopkins
-
Publication number: 20200334569Abstract: Techniques are provided for selection of machine learning algorithms based on performance predictions by using hyperparameter predictors. In an embodiment, for each mini-machine learning model (MML model) of a plurality of MML models, a respective hyperparameter predictor set that predicts a respective set of hyperparameter settings for a first data set is trained. Each MML model represents a respective reference machine learning model (RML model) of a plurality of RML models. A first plurality of data set samples is generated from the first data set. A first plurality of first meta-feature sets is generated, each first meta-feature set describing a respective first data set sample of said first plurality. A respective target set of hyperparameter settings are generated for said each MML model using a hypertuning algorithm. The first plurality of first meta-feature sets and the respective target set of hyperparameter settings are used to train the respective hyperparameter predictor set.Type: ApplicationFiled: April 18, 2019Publication date: October 22, 2020Inventors: Hesam Fathi Moghadam, Sandeep Agrawal, Venkatanathan Varadarajan, Anatoly Yakovlev, Sam Idicula, Nipun Agarwal
-
Publication number: 20200327448Abstract: Herein are techniques for exploring hyperparameters of a machine learning model (MLM) and to train a regressor to predict a time needed to train the MLM based on a hyperparameter configuration and a dataset. In an embodiment that is deployed in production inferencing mode, for each landmark configuration, each containing values for hyperparameters of a MLM, a computer configures the MLM based on the landmark configuration and measures time spent training the MLM on a dataset. An already trained regressor predicts time needed to train the MLM based on a proposed configuration of the MLM, dataset meta-feature values, and training durations and hyperparameter values of landmark configurations of the MLM. When instead in training mode, a regressor in training ingests a training corpus of MLM performance history to learn, by reinforcement, to predict a training time for the MLM for new datasets and/or new hyperparameter configurations.Type: ApplicationFiled: April 15, 2019Publication date: October 15, 2020Inventors: ANATOLY YAKOVLEV, VENKATANATHAN VARADARAJAN, SANDEEP AGRAWAL, HESAM FATHI MOGHADAM, SAM IDICULA, NIPUN AGARWAL
-
Publication number: 20200327357Abstract: The present invention relates to dimensionality reduction for machine learning (ML) models. Herein are techniques that individually rank features and combine features based on their rank to achieve an optimal combination of features that may accelerate training and/or inferencing, prevent overfitting, and/or provide insights into somewhat mysterious datasets. In an embodiment, a computer ranks features of datasets of a training corpus. For each dataset and for each landmark percentage, a target ML model is configured to receive only a highest ranking landmark percentage of features, and a landmark accuracy achieved by training the ML model with the dataset is measured. Based on the landmark accuracies and meta-features values of the dataset, a respective training tuple is generated for each dataset. Based on all of the training tuples, a regressor is trained to predict an optimal amount of features for training the target ML model.Type: ApplicationFiled: August 21, 2019Publication date: October 15, 2020Inventors: TOMAS KARNAGEL, SAM IDICULA, HESAM FATHI MOGHADAM, NIPUN AGARWAL